February 2024 arXiv papers — page 44

Fabrício Lustosa, Roberto M. Serra, Luciano S. Cruz, Breno Marques

The development of techniques that reduce experimental complexity and minimize errors is an utmost importance for modeling quantum channels. In general, quantum simulators are focused on universal algorithms, whose practical implementation requires extra qubits necessary to control the quantum operations. In contrast, our technique is based on finding a way

Zhen Jia, Philipp Christmann, Gerhard Weikum

Temporal question answering (QA) involves time constraints, with phrases such as "... in 2019" or "... before COVID". In the former, time is an explicit condition, in the latter it is implicit. State-of-the-art methods have limitations along three dimensions. First, with neural inference, time constraints are merely soft-matched, giving room to invalid or in

Zhishuai Liu, Pan Xu

We study off-dynamics Reinforcement Learning (RL), where the policy is trained on a source domain and deployed to a distinct target domain. We aim to solve this problem via online distributionally robust Markov decision processes (DRMDPs), where the learning algorithm actively interacts with the source domain while seeking the optimal performance under the w

Ruoyu Sun, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su

As a model-free algorithm, deep reinforcement learning (DRL) agent learns and makes decisions by interacting with the environment in an unsupervised way. In recent years, DRL algorithms have been widely applied by scholars for portfolio optimization in consecutive trading periods, since the DRL agent can dynamically adapt to market changes and does not rely

Yan Luo, Zhuoyue Wan, Yuzhong Chen, Gengchen Mai

Understanding the link between urban planning and commuting flows is crucial for guiding urban development and policymaking. This research, bridging computer science and urban studies, addresses the challenge of integrating these fields with their distinct focuses. Traditional urban studies methods, like the gravity and radiation models, often underperform i

A. N. Korshunov, A. S. Sukhanov, S. Gebel, M. S. Pavlovskii

EuAl$_4$ is a rare earth intermetallic in which competing itinerant and/or indirect exchange mechanisms give rise to a complex magnetic phase diagram, including a centrosymmetric skyrmion lattice. These phenomena arise not in the tetragonal parent structure but in the presence of a charge density wave (CDW), which lowers the crystal symmetry and renormalizes

Y. Brihaye, F. Buisseret

We construct Q-ball solutions from a model consisting of one massive scalar field $\xi$ and one massive complex scalar field $\phi$ interacting via the cubic couplings $g_1 \xi \phi^{*} \phi + g_2 \xi^3$, typical of Henon-Heiles-like potentials. Although being formally simple, these couplings allow for Q-balls. In one spatial dimension, analytical solutions

J. A. S. Fortunato, P. H. R. S. Moraes, E. Brito, J. G. de Lima Júnior

In the present work, we obtain the hydrostatic equilibrium configurations of neutron stars in the recently proposed $f(R,\mathcal{L},T)$ theory of gravity, for which $R$ is the Ricci scalar, $\mathcal{L}$ is the matter lagrangian density, $T$ is the trace of the energy-momentum tensor and $f$ is a function of the argument. This theory emerges in the present

Enugala Sumanth Nani, Thomas Philippe, Mathis Plapp

A phase-field model for diffusion-limited crystal growth is formulated that is capable of handling highly anisotropic interfaces. It uses a Willmore regularization that yields corners of finite size. An asymptotic analysis reveals that Herring's law is recovered for the advancing surfaces. The model is validated by conducting simulations of dendritic growth

Bernardo Esteves, Miguel Vasco, Francisco S. Melo

We contribute NeuralSolver, a novel recurrent solver that can efficiently and consistently extrapolate, i.e., learn algorithms from smaller problems (in terms of observation size) and execute those algorithms in large problems. Contrary to previous recurrent solvers, NeuralSolver can be naturally applied in both same-size problems, where the input and output

Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli

Inverse reinforcement learning (IRL) aims to recover the reward function of an expert agent from demonstrations of behavior. It is well-known that the IRL problem is fundamentally ill-posed, i.e., many reward functions can explain the demonstrations. For this reason, IRL has been recently reframed in terms of estimating the feasible reward set (Metelli et al

Jake Bruce, Michael Dennis, Ashley Edwards, Jack Parker-Holder

We introduce Genie, the first generative interactive environment trained in an unsupervised manner from unlabelled Internet videos. The model can be prompted to generate an endless variety of action-controllable virtual worlds described through text, synthetic images, photographs, and even sketches. At 11B parameters, Genie can be considered a foundation wor

Monika Grewal, Henrike Westerveld, Peter A. N. Bosman, Tanja Alderliesten

Deformable image registration (DIR) involves optimization of multiple conflicting objectives, however, not many existing DIR algorithms are multi-objective (MO). Further, while there has been progress in the design of deep learning algorithms for DIR, there is no work in the direction of MO DIR using deep learning. In this paper, we fill this gap by combinin

Cody Rushing, Neel Nanda

Prior interpretability research studying narrow distributions has preliminarily identified self-repair, a phenomena where if components in large language models are ablated, later components will change their behavior to compensate. Our work builds off this past literature, demonstrating that self-repair exists on a variety of models families and sizes when

V. Kott, M. Mühlhauser, J. A. Koziol, K. P. Schmidt

We investigate the quantum robustness of the topological order in the toric code on the honeycomb lattice in the presence of a uniform parallel field. For a field in $z$-direction, the low-energy physics is in the flux-free sector and can be mapped to the transverse-field Ising model on the honeycomb lattice. One finds a second-order quantum phase transition

Matin Fallahi, Patricia Arias Cabarcos, Thorsten Strufe

Passwords remain a widely-used authentication mechanism, despite their well-known security and usability limitations. To improve on this situation, next-generation authentication mechanisms, based on behavioral biometric factors such as eye movement and brainwave have emerged. However, their usability remains relatively under-explored. To fill this gap, we c

Alois Pichler

Higher order risk measures are stochastic optimization problems by design, and for this reason they enjoy valuable properties in optimization under uncertainties. They nicely integrate with stochastic optimization problems, as has been observed by the intriguing concept of the risk quadrangles, for example. Stochastic dominance is a binary relation for rando

Fedor Simkovic, Martin Leib, Francisco Revson F. Pereira

We perform an extended numerical search for practical fermion-to-qubit encodings with error correcting properties. Ideally, encodings should strike a balance between a number of the seemingly incompatible attributes, such as having a high minimum distance, low-weight fermionic logical operators, a small qubit to fermionic mode ratio and a simple qubit connec

Fabrizio Sgobba, Danilo Triggiani, Vincenzo Tamma, Paolo De Natale

This paper presents an all-fiber telecom-range optical gyroscope employing a spontaneous parametric down conversion crystal to produce ultra-low intensity thermal light by tracing-out one of the heralded photons. The prototype exhibits a detection limit on photon delay measurements of $249$ zs over a $72$ s averaging time and 26 zs in differential delay meas

Luigi Scurto, Helena Pais, Francesca Gulminelli

In this work we present general predictions for the static observables of neutron stars (NSs) under the hypothesis of a purely nucleonic composition of the ultra-dense baryonic matter, using Bayesian inference on a very large parameter space conditioned by both astrophysical and nuclear physics constraints. The equation of states are obtained using a unified

Giulia Lafratta, Bernd Porr, Christopher Chandler, Alice Miller

Living organisms interact with their surroundings in a closed-loop fashion, where sensory inputs dictate the initiation and termination of behaviours. Even simple animals are able to develop and execute complex plans, which has not yet been replicated in robotics using pure closed-loop input control. We propose a solution to this problem by defining a set of

Daniel R Chavas, John A Knaff, Philip J Klotzbach

Minimum central pressure ($P_{min}$) is an integrated measure of the tropical cyclone wind field and is known to be a useful indicator of storm damage potential. A simple model that predicts $P_{min}$ from routinely-estimated quantities, including storm size, would be of great value. Here we present a simple linear empirical model for predicting $P_{min}$ fr

Juan Pablo Aguilera, Fedor Pakhomov

For each $n\in\mathbb{N}$, let $[n]\phi$ mean "the sentence $\phi$ is true in all $\Sigma_{n+1}$-correct transitive sets." Assuming G\"odel's axiom $V = L$, we prove the following graded variant of Solovay's completeness theorem: the set of formulas valid under this interpretation is precisely the set of theorems of the linear provability logic GLP.3. We als

A. Moerman, K. Karatsu, S. J. C. Yates, R. Huiting

Integrated superconducting spectrometers (ISSs) for wideband sub-mm astronomy utilise quasi-optical systems for coupling radiation from the telescope to the instrument. Misalignment in these systems is detrimental to the system performance. The common method of using an optical laser to align the quasi-optical components requires accurate alignment of the la

Paul-Gerhard Reinhard, Jared O'Neal, Stefan M. Wild, Witold Nazarewicz

The Fayans energy density functional (EDF) has been very successful in describing global nuclear properties (binding energies, charge radii, and especially differences of radii) within nuclear density functional theory. In a recent study, supervised machine learning methods were used to calibrate the Fayans EDF. Building on this experience, in this work we e

Fiona Raphaela Spuler, Marlene Kretschmer, Yevgeniya Kovalchuk, Magdalena Alonso Balmaseda

Weather regimes are recurrent and persistent large-scale atmospheric circulation patterns that modulate the occurrence of local impact variables such as extreme precipitation. In their capacity as mediators between long-range teleconnections and these local extremes, they have shown potential for improving sub-seasonal forecasting as well as long-term climat

F. Cosco, R. Tuovinen, N. Lo Gullo

This work reports the study of the spectral properties of an open interacting system by solving the Generalized Kadanoff-Baym Ansatz (GKBA) master equation for the single-particle density matrix, namely the time-diagonal lesser Green function. To benchmark its validity, the solution obtained within the GKBA is compared with the solution of the Dyson equation

Stamatios Stamatatelopoulos, Shahroz Khan, Panagiotis Kaklis

Reducing the dimensionality and uncertainty of design spaces is a key prerequisite for shape optimisation in computationally intensive fluid problems. However, running these analyses at an offline stage itself poses a computationally demanding task. In this work, we propose a unique framework for the inexpensive implementation of sensitivity analyses for red

Shailesh Chandrasekharan, Son T. Nguyen, Thomas R. Richardson

In this work we introduce a worldline based fermion Monte Carlo algorithm for studying few body quantum mechanics of self-interacting fermions in the Hamiltonian lattice formulation. Our motivation to construct the method comes from our interest in studying renormalization of chiral nuclear effective field theory with lattice regularization. In particular we

Fang Fang, Kenneth Wang, Vincent S. Liu, Yu Wang

At continuous phase transitions, quantum many-body systems exhibit scale-invariance and complex, emergent universal behavior. Most strikingly, at a quantum critical point, correlations decay as a power law, with exponents determined by a set of universal scaling dimensions. Experimentally probing such power-law correlations is extremely challenging, owing to

Pieter J. van Essen, Zhonghui Nie, Brian de Keijzer, Peter M. Kraus

Optical modulation of high-harmonics generation in solids enables the detection of material properties such as the band structure and promising new applications such as super-resolution imaging in semiconductors. Various recent studies have shown optical modulation of high-harmonics generation in solids, in particular, suppression of high-harmonics generatio

Anja Delić, Matej Grcić, Siniša Šegvić

Outlier detection is an essential capability in safety-critical applications of supervised visual recognition. Most of the existing methods deliver best results by encouraging standard closed-set models to produce low-confidence predictions in negative training data. However, that approach conflates prediction uncertainty with recognition of the negative cla

I. E. Papadakis, V. Binas-Valavanis

We study the X-ray power spectrum of Active Galactic Nuclei (AGN) to investigate whether Seyfert I and II power spectra are similar or not, whether the AGN variability depends on black hole mass and accretion rate, and to compare the AGN power spectra with the Galactic X-ray black hole binaries power-spectra. We used 14-195 keV band light curves from the 157

Henri Derycke, Mark Dukes, Yvan Le Borgne

This paper studies sorted recurrent configurations of the Abelian sandpile model on the complete split graph. We introduce two natural toppling processes, CTI and ITC toppling, on the recurrent configurations and use these to define two toppling delay statistics, wtopple$_{CTI}$ and wtopple$_{ITC}$. These new toppling delay statistics are time-weighted sums

Anthony Gandon, Alberto Baiardi, Pauline Ollitrault, Ivano Tavernelli

We introduce a novel computational framework for excited-states molecular quantum dynamics simulations driven by quantum computing-based electronic-structure calculations. This framework leverages the fewest-switches surface-hopping method for simulating the nuclear dynamics, and calculates the required excited-state transition properties with different flav

Xiaowei Zhao, Yong Zhou, Xiujuan Xu

Aspect Sentiment Triple Extraction (ASTE) is an emerging task in fine-grained sentiment analysis. Recent studies have employed Graph Neural Networks (GNN) to model the syntax-semantic relationships inherent in triplet elements. However, they have yet to fully tap into the vast potential of syntactic and semantic information within the ASTE task. In this work

Erwan Lanneau, Livio Liechti, Chi Cheuk Tsang

We show that the stretch factor $\lambda(f)$ of an orientation-reversing fully-punctured pseudo-Anosov map $f$ on a finite-type orientable surface $S$, with $-\chi(S) \geq 4$ and having at least two puncture orbits, satisfies the inequality $\lambda(f)^{-\chi(S)} \geq \sigma^2$, where $\sigma=1+\sqrt{2}$ is the silver ratio. We provide examples showing that

John R. Helliwell, James R. Hester, Loes Kroon-Batenburg, Brian McMahon

The hardware for data archiving has expanded capacities for digital storage enormously in the past decade or more. This article charts the efforts of IUCr to facilitate discussions and plans relating to raw data archiving and reuse within the various communities of crystallography, diffraction, and scattering.

Jun Wang, Guocheng He, Yiannis Kantaros

This paper addresses task planning problems for language-instructed robot teams. Tasks are expressed in natural language (NL), requiring the robots to apply their capabilities at various locations and semantic objects. Several recent works have addressed similar planning problems by leveraging pre-trained Large Language Models (LLMs) to design effective mult

Zirui Zhu, Yong Liu, Zangwei Zheng, Huifeng Guo

Click-Through Rate (CTR) prediction holds paramount significance in online advertising and recommendation scenarios. Despite the proliferation of recent CTR prediction models, the improvements in performance have remained limited, as evidenced by open-source benchmark assessments. Current researchers tend to focus on developing new models for various dataset

E. Carlini, R. Ferretti, S. Preda, M. Semplice

We propose a high order numerical scheme for time-dependent first order Hamilton--Jacobi--Bellman equations. In particular we propose to combine a semi-Lagrangian scheme with a Central Weighted Non-Oscillatory reconstruction. We prove a convergence result in the case of state- and time-independent Hamiltonians. Numerical simulations are presented in space di

CMS Collaboration

Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class

Zhuojun Quan, Yuanyuan Lin, Kani Chen, Wen Yu

Semi-supervised learning has received increasingly attention in statistics and machine learning. In semi-supervised learning settings, a labeled data set with both outcomes and covariates and an unlabeled data set with covariates only are collected. We consider an inference problem in semi-supervised settings where the outcome in the labeled data is binary a

Beatrix C. Hiesmayr, Marc-Thorsten Hütt

A recent trend in mathematical modeling is to publish the computer code together with the research findings. Here we explore the formal question, whether and in which sense a computer implementation is distinct from the mathematical model. We argue that, despite the convenience of implemented models, a set of implicit assumptions is perpetuated with the impl

Yurim Jeon, E In Son, Seung-Woo Seo

In this study, we address the off-road traversability estimation problem, that predicts areas where a robot can navigate in off-road environments. An off-road environment is an unstructured environment comprising a combination of traversable and non-traversable spaces, which presents a challenge for estimating traversability. This study highlights three prim

János Kollár, Ziquan Zhuang

We give a lower bound for the essential dimension of isogenies of complex abelian varieties. The bound is sharp in many cases. In particular, the multiplication-by-$m$ map is incompressible for every $m\geq 2$, confirming a conjecture of Brosnan.

Fabio Leotta, Jan Giesselmann

We give a priori error estimates of second order in time fully explicit Runge-Kutta discontinuous Galerkin schemes using upwind fluxes to smooth solutions of scalar fractional conservation laws in one space dimension. Under the time step restrictions $\tau\leq c h$ for piecewise linear and $\tau\lesssim h^{4/3}$ for higher order finite elements, we prove a c

Amanda K. Navine, Tom Denton, Matthew J. Weldy, Patrick J. Hart

Passive acoustic monitoring (PAM) studies generate thousands of hours of audio, which may be used to monitor specific animal populations, conduct broad biodiversity surveys, detect threats such as poachers, and more. Machine learning classifiers for species identification are increasingly being used to process the vast amount of audio generated by bioacousti

J. E. San Soucie, H. M. Sosik, Y. Girdhar

We present the Streaming Gaussian Dirichlet Random Field (S-GDRF) model, a novel approach for modeling a stream of spatiotemporally distributed, sparse, high-dimensional categorical observations. The proposed approach efficiently learns global and local patterns in spatiotemporal data, allowing for fast inference and querying with a bounded time complexity.

A. Bertin, L. Kiefer, P. Becker, L. Bohatý

We present temperature-dependent single-crystal diffraction results on seven antifluorite-type $A_2MeX_6$ compounds with $Me$=Os or Ir: K$_2$OsCl$_6$, $A_2$OsBr$_6$ with $A$=K, Rb, Cs and NH$_4$, and K$_2$Ir$X_6$ with $X$=Cl and Br. The structural transitions in this family arise from $MeX_6$ octahedron rotations that generate a rich variety of symmetries de

Lloyd Fung, Urban Fasel, Matthew P. Juniper

We propose a fast probabilistic framework for identifying differential equations governing the dynamics of observed data. We recast the SINDy method within a Bayesian framework and use Gaussian approximations for the prior and likelihood to speed up computation. The resulting method, Bayesian-SINDy, not only quantifies uncertainty in the parameters estimated

Alessandra Bianchi, Giacomo Passuello

In this paper, we are interested in the mixing behaviour of simple random walks on inhomogeneous directed graphs. We focus our study on the Chung-Lu digraph, which is an inhomogeneous network that generalizes the Erd\H{o}s-R\'enyi digraph. In particular, under the Chung-Lu model, edges are included in the graph independently and according to given Bernoulli

Filippo Sarti, Alessio Savini

We give a notion of boundary pair $(\mathcal{B}_-,\mathcal{B}_+)$ for measured groupoids which generalizes the one introduced by Bader and Furman \cite{BF14} for locally compact groups. In the case of a semidirect groupoid $\mathcal{G}=\Gamma \ltimes X$ obtained by a probability measure preserving action $\Gamma \curvearrowright X$ of a locally compact group

Krishnendu De, Uday Chand De, Ljubica Velimirovic

In this paper we assume that a perfect fluid is the source of the gravitational field while analyzing the solutions to the Einstein field equations.

Oleh Melnyk, Patricia Römer

Ptychography is a computational imaging technique that aims to reconstruct the object of interest from a set of diffraction patterns. Each of these is obtained by a localized illumination of the object, which is shifted after each illumination to cover its whole domain. As in the resulting measurements the phase information is lost, ptychography gives rise t

Sébastien Herbreteau, Charles Kervrann

Image denoising is probably the oldest and still one of the most active research topic in image processing. Many methodological concepts have been introduced in the past decades and have improved performances significantly in recent years, especially with the emergence of convolutional neural networks and supervised deep learning. In this paper, we propose a

Zekang Yang, Wang Zeng, Sheng Jin, Chen Qian

Automated machine learning (AutoML) is a collection of techniques designed to automate the machine learning development process. While traditional AutoML approaches have been successfully applied in several critical steps of model development (e.g. hyperparameter optimization), there lacks a AutoML system that automates the entire end-to-end model production

Zijie J. Wang, Chinmay Kulkarni, Lauren Wilcox, Michael Terry

Prompt-based interfaces for Large Language Models (LLMs) have made prototyping and building AI-powered applications easier than ever before. However, identifying potential harms that may arise from AI applications remains a challenge, particularly during prompt-based prototyping. To address this, we present Farsight, a novel in situ interactive tool that hel

Hajime Otsuka, Yutaka Sakamura

We investigate the cosmological expansion of the 3D space in a 6D model compactified on a sphere, beyond the 4D effective theory analysis. We focus on a case that the initial temperature is higher than the compactification scale. In such a case, the pressure for the compact space affects the moduli dynamics and induces the moduli oscillation even if they are

Siqi Qiao, Nils von den Driesch, Xi Chen, Stefan Trellenkamp

ZnSe and related materials like ZnMgSe and ZnCdSe are promising II-VI host materials for optically mediated quantum information technology such as single photon sources or spin qubits. Integrating these heterostructures into photonic crystal (PC) cavities enables further improvements, for example realizing Purcell-enhanced single photon sources with increase

Matthias Bentert, Fedor V. Fomin, Petr A. Golovach

We examine the possibility of approximating Maximum Vertex-Disjoint Shortest Paths. In this problem, the input is an edge-weighted (directed or undirected) $n$-vertex graph $G$ along with $k$ terminal pairs $(s_1,t_1),(s_2,t_2),\ldots,(s_k,t_k)$. The task is to connect as many terminal pairs as possible by pairwise vertex-disjoint paths such that each path i

Alessandro G. Bottero, Carlos E. Luis, Julia Vinogradska, Felix Berkenkamp

We consider a sequential decision making task, where the goal is to optimize an unknown function without evaluating parameters that violate an a~priori unknown (safety) constraint. A common approach is to place a Gaussian process prior on the unknown functions and allow evaluations only in regions that are safe with high probability. Most current methods rel

Zhi-Kai Zhu, Min Fang, Zu-Jia Lu, Junzhi Wang

The kinematic information of the Radcliffe Wave (RW) is essential for determining its existence and gaining insights into its origin and evolution. In this work, we present an accurate measurement of the vertical velocity ($V_Z$) of RW by incorporating the radial velocity (RV) measures through two methods, which is crucial but was neglected previously. First

Alfredo De la Fuente, Saurabh Singh, Johannes Ballé

We introduce a lightweight, flexible and end-to-end trainable probability density model parameterized by a constrained Fourier basis. We assess its performance at approximating a range of multi-modal 1D densities, which are generally difficult to fit. In comparison to the deep factorized model introduced in [1], our model achieves a lower cross entropy at a

Kento Imaizumi, Hideaki Iiduka

The performance of stochastic gradient descent (SGD), which is the simplest first-order optimizer for training deep neural networks, depends on not only the learning rate but also the batch size. They both affect the number of iterations and the stochastic first-order oracle (SFO) complexity needed for training. In particular, the previous numerical results

Sergei Bogdanov, Alexandre Constantin, Timothée Bernard, Benoit Crabbé

Large Language Models (LLMs) have shown impressive abilities in data annotation, opening the way for new approaches to solve classic NLP problems. In this paper, we show how to use LLMs to create NuNER, a compact language representation model specialized in the Named Entity Recognition (NER) task. NuNER can be fine-tuned to solve downstream NER problems in a

Istiak Ahmed, F. Parvin, R. S. Islam, S. H. Naqib

We investigated the structural, elastic, electronic, vibrational, optical, thermodynamic and a number of thermophysical properties of W2N3 in this study using DFT based formalisms. The mechanical and dynamical stabilities have been confirmed. The Pugh and Poisson ratios are located quite close to the brittle to ductile borderline. The electronic band structu

Eduardo Viegas, Tim S. Evans

Researchers have been long preoccupied with the measuring and monitoring of economic and social deprivation at small scales, neighbourhood, level in order to provide official government agencies and policy makers with more precise data insights. Whist valuable methodologies have been developed, the exercise of data collection associated with these methods te

Aitor Toichoa Eyam, Jose L. Martinez Lastra

Photo-realistic avatar is a modern term referring to the digital asset that represents a human in computer graphic advanced systems such as video games and simulation tools. These avatars utilize the advances in graphic technologies in both software and hardware aspects. While photo-realistic avatars are increasingly used in industrial simulations, represent

Krishnendu De, Mohammad Nazrul Islam Khan, Uday Chand De

In this article, we examine gradient type Ricci solitons and $(m,\tau)$-quasi Einstein solitons in generalized Robertson-Walker ($GRW$) spacetimes. Besides, we demonstrate that in this scenario the $GRW$ spacetime presents the Robertson-Walker ($RW$) spacetime and the perfect fluid ($PF$) spacetime presents the phantom era. Consequently, we show that if a $G

Nahuel Nuñez, Enio Lima, Marcelo Vásquez Mansilla, Gerardo F. Goya

Ferrite nanoparticles serve as potent heterogeneous Fenton-like catalysts, producing reactive oxygen species (ROS) for decomposing organic pollutants. We investigated the impact of temperature and copper content on the catalytic activity of nanoparticles with different oxidation states of iron. Via solvothermal synthesis, we fabricated copper-doped magnetite

Nitesh Kumar, Usashi Chatterjee, Steven Schockaert

Conceptual spaces represent entities in terms of their primitive semantic features. Such representations are highly valuable but they are notoriously difficult to learn, especially when it comes to modelling perceptual and subjective features. Distilling conceptual spaces from Large Language Models (LLMs) has recently emerged as a promising strategy, but exi

N. V. Krishnendu, Sumanta Chakraborty

Recent gravitational wave (GW) observations have enabled us to look beyond the standard paradigm of gravitational physics, namely general relativity (GR). Along with the mass and the angular momentum, which typical astrophysical black holes (BHs) are endowed with, theories beyond GR generically induce `charge' to these BHs. Notably, for BHs carrying the extr

Chenyu Li, Bing Zhang, Danfeng Hong, Jing Yao

Current hyperspectral anomaly detection (HAD) benchmark datasets suffer from low resolution, simple background, and small size of the detection data. These factors also limit the performance of the well-known low-rank representation (LRR) models in terms of robustness on the separation of background and target features and the reliance on manual parameter se

Jinfei Wang, Yi Ma, Rahim Tafazolli

While iterative matrix inversion methods excel in computational efficiency, memory optimization, and support for parallel and distributed computing when managing large matrices, their limitations are also evident in multiple-input multiple-output (MIMO) fading channels. These methods encounter challenges related to slow convergence and diminished accuracy, e

Ryan L'Abbate, Anthony D'Onofrio, Samuel Stein, Samuel Yen-Chi Chen

Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posi

Bruno Gavranović, Paul Lessard, Andrew Dudzik, Tamara von Glehn

We present our position on the elusive quest for a general-purpose framework for specifying and studying deep learning architectures. Our opinion is that the key attempts made so far lack a coherent bridge between specifying constraints which models must satisfy and specifying their implementations. Focusing on building a such a bridge, we propose to apply c

Sana Hafeez, Runze Cheng, Lina Mohjazi, Muhammad Ali Imran

Emergency communication is critical but challenging after natural disasters when ground infrastructure is devastated. Unmanned aerial vehicles (UAVs) offer enormous potential for agile relief coordination in these scenarios. However, effectively leveraging UAV fleets poses additional challenges around security, privacy, and efficient collaboration across res

Michael A. Ramirez-Sierra, Thomas R. Sokolowski

Understanding how multicellular organisms reliably orchestrate cell-fate decisions is a central challenge in developmental biology. This is particularly intriguing in early mammalian development, where early cell-lineage differentiation arises from processes that initially appear cell-autonomous but later materialize reliably at the tissue level. In this stu

Nidhi Gupta

For any sheaf of sets $\mathcal F$ on $Sm/k$, it is well known that the universal $\mathbb A^1$-invariant quotient of $\mathcal F$ is given as the colimit of sheaves $\mathcal S^n(\mathcal F)$ where $\mathcal S(F)$ is the sheaf of naive $\mathbb A^1$-connected components of $\mathcal F$. We show that these infinite iterations of naive $\mathbb A^1$-connected

Chenguang Wang, Xuanhao Pan, Tianshu Yu

Multi-task learning (MTL) aims to leverage shared information among tasks to improve learning efficiency and accuracy. However, MTL often struggles to effectively manage positive and negative transfer between tasks, which can hinder performance improvements. Task grouping addresses this challenge by organizing tasks into meaningful clusters, maximizing benef

David Brizuela, Marco de Cesare, Araceli Soler Oficial

We present a formalism to study linear perturbations of bimetric gravity on any spherically symmetric background, including dynamical spacetimes. The setup is based on the Gerlach-Sengupta formalism for general relativity. Each of the two background metrics is written as a warped product between a two-dimensional Lorentzian metric and the round metric of the

Weichen Zhao, Chenguang Wang, Xinyan Wang, Congying Han

This paper presents an analytical study of the oversmoothing issue in diffusion-based Graph Neural Networks (GNNs). Generalizing beyond extant approaches grounded in random walk analysis or particle systems, we approach this problem through operator semigroup theory. This theoretical framework allows us to rigorously prove that oversmoothing is intrinsically

Oliver Thim, Andreas Ekström, Christian Forssén

We predict neutron-proton scattering cross-sections and polarization observables up to next-to-next-to-next-to leading order in a renormalization-group invariant description of the strong nucleon-nucleon interaction. Low-energy constants are calibrated to phase shifts, sub-leading corrections are computed in distorted-wave perturbation theory, and we employ

Jianhong Wang

Multi-agent reinforcement learning is an area of rapid advancement in artificial intelligence and machine learning. One of the important questions to be answered is how to conduct credit assignment in a multi-agent system. There have been many schemes designed to conduct credit assignment by multi-agent reinforcement learning algorithms. Although these credi

Octavian Micu

We investigate the dynamical stability of bootstrapped Newtonian stars following homologous adiabatic perturbations, focusing on objects of low or intermediate compactness. The results show that for stars with homogeneous densities these perturbations induce some oscillatory behaviour regardless of their compactness, density and adiabatic index, which makes

Daan Bon, Gautam Pai, Gijs Bellaard, Olga Mula

The roto-translation group SE2 has been of active interest in image analysis due to methods that lift the image data to multi-orientation representations defined on this Lie group. This has led to impactful applications of crossing-preserving flows for image de-noising, geodesic tracking, and roto-translation equivariant deep learning. In this paper, we deve

Francis Engelmann, Ayca Takmaz, Jonas Schult, Elisabetta Fedele

This report provides an overview of the challenge hosted at the OpenSUN3D Workshop on Open-Vocabulary 3D Scene Understanding held in conjunction with ICCV 2023. The goal of this workshop series is to provide a platform for exploration and discussion of open-vocabulary 3D scene understanding tasks, including but not limited to segmentation, detection and mapp

Maarten Lathouwers, Thomas Witdouck

Let $G$ be a group and $\varphi$ an automorphism of $G$. Two elements $x,y \in G$ are said to be $\varphi$-conjugate if there exists a third element $z \in G$ such that $z x \varphi(z)^{-1} = y$. Being $\varphi$-conjugate defines an equivalence relation on $G$. The group $G$ is said to have the $R_{\infty}$-property if all its automorphisms $\varphi$ have in

Mart van Baalen, Andrey Kuzmin, Ivan Koryakovskiy, Markus Nagel

In this work we show that the size versus accuracy trade-off of neural network quantization can be significantly improved by increasing the quantization dimensionality. We propose the GPTVQ method, a new fast method for post-training vector quantization (VQ) that scales well to Large Language Models (LLMs). Our method interleaves quantization of one or more

Hepeng Yao, Luca Tanzi, Laurent Sanchez-Palencia, Thierry Giamarchi

Disorder or quasi-disorder is known to favor the localization in many-body Bose systems. Here in contrast, we demonstrate an anomalous delocalization effect induced by incommensurability in quasiperiodic lattices. Loading ultracold atoms in two shallow periodic lattices with equal amplitude and either equal or incommensurate spatial periods, we show the onse

Felix Röhrle, Martin Ulirsch

A bimatroid is a matroid-like generalization of the collection of regular minors of a matrix. In this article, we use the theory of Lorentzian polynomials to study the logarithmic concavity of natural sequences associated to bimatroids. Bimatroids can be used to characterize morphisms of matroids and this observation (originally due to Kung) allows us to pro

Félix Helluin, Léonie Canet, Anna Minguzzi

In the absence of vortices or phase slips, the phase dynamics of exciton-polariton condensates was shown to map onto the Kardar-Parisi-Zhang (KPZ) equation, which describes the stochastic growth of a classical interface. This implies that the coherence of such non-equilibrium quasi-condensates decays in space and time following stretched exponentials, charac

Juan L. Valerdi

Understanding the relationship between the depth of a neural network and its representational capacity is a central problem in deep learning theory. In this work, we develop a geometric framework to analyze the expressivity of ReLU networks with the notion of depth complexity for convex polytopes. The depth of a polytope recursively quantifies the number of

Houssam Kharouji, Abdellatif Hasnaoui, Achraf Atila

The quest for hard and tough transparent oxide glasses is at the core of glass science and technology. Aluminosilicate glasses exhibiting nanoscale phase separation emerge as promising candidates for such materials. Nevertheless, proper control of the phase separation represents a daunting challenge due to its elusive origins. Here we employ large-scale mole

Anis Koubaa, Adel Ammar, Lahouari Ghouti, Omar Najar

The predominance of English and Latin-based large language models (LLMs) has led to a notable deficit in native Arabic LLMs. This discrepancy is accentuated by the prevalent inclusion of English tokens in existing Arabic models, detracting from their efficacy in processing native Arabic's intricate morphology and syntax. Consequently, there is a theoretical

Michele Coti Zelati, Augusto Del Zotto, Klaus Widmayer

This article explores the stability of stratified Couette flow in the viscous $3d$ Boussinesq equations. In this system, mixing effects arise from the shearing background, and gravity acts as a restoring force leading to dispersive internal gravity waves. These mechanisms are of fundamentally different nature and relevant in complementary dynamical regimes.

Francisco Cirelli, Pablo Groisman, Ruojun Huang, Hernán Vivas

We consider the Kuramoto model on a graph with nodes given by $n$ i.i.d. points uniformly distributed on the $d$ dimensional torus. Two nodes are declared neighbors if they are at distance less than $\epsilon$. We prove a scaling limit for this model in compact time intervals as $n\to\infty$ and $\epsilon \to 0$ such that $\epsilon^{d+2}n/\log n \to \infty$.

Xuhua He, Sian Nie, Qingchao Yu

In this paper, we study the affine Deligne--Lusztig variety $X(\mu,b)_K$ and classify all quadruples $(\mathbf{G}, \mu, b, K)$ with $\dim X(\mu, b)_K=0$. This question was first asked by Rapoport in 2005, who also made an explicit conjecture in the hyperspecial level. We prove that $\dim X(\mu,b)_K=0$ if and only if, up to certain Hodge-Newton decomposition

Fei Liu, Xi Lin, Zhenkun Wang, Qingfu Zhang

Vehicle routing problems (VRPs), which can be found in numerous real-world applications, have been an important research topic for several decades. Recently, the neural combinatorial optimization (NCO) approach that leverages a learning-based model to solve VRPs without manual algorithm design has gained substantial attention. However, current NCO methods ty